Not only can we use marginal likelihoods to learn about evolutionary models,
but we can also use them to learn important lessons about our data.
[1]} explored six different genomic data sets
that were collected to infer phylogenetic relationships within Amniota.
For each locus across all six data sets, they used the stepping-stone method
[2]} to approximate the marginal likelihood of models that included
or excluded a particular branch (bipartition) in the amniote tree.
This allowed [1]} to calculate, for each
gene, Bayes factors as measures of support for or against particular
relationships, some of which are uncontroversial (e.g., the monophyly of birds)
and others contentious (e.g., the placement of turtles).
